Time series data refers to sequences of data points measured over a span of time, often spaced at uniform time intervals. Time series data is often stored on a remote server known as historian. The historian is responsible for collecting raw time series data and cleaning raw time series data. For analysis and query purposes, time series data is fetched from the historian. However, due to the ever increasing size of time series data, retrieval of time series data is an expensive operation in terms of network resources.
There have been several approaches to optimize the retrieval of time series data. One such approach (US 20110153603, Adiba et al.; US 20110167486, Ayloo et al.) suggests the usage of a cache memory for holding recent time series data. However, this approach is often inefficient as the cached time series data is not reflective of the actual time series data. Time series data is mutable since corrections are often made to the time series data at a later point of time. When a correction is made to the actual time series data, the cached data is no longer valid. Since the current approach does not take into account the mutable nature of time series data, the current approach is inefficient.
Therefore, in light of the above discussion, there is a need for a method and system which overcomes all the above stated problems.